Systems and methods for subsurface oil recovery optimization

ABSTRACT

Systems and methods for subsurface secondary and/or tertiary oil recovery optimization based on either a short term, medium term or long term optimization analysis of selected zones, wells, patterns/clusters and/or fields.

CROSS-REFERENCE TO RELATED APPLICATIONS STATEMENT REGARDING FEDERALLYSPONSORED RESEARCH

This application is a continuation of U.S. patent application Ser. No.14/002,496 filed Aug. 30, 2013, which claims priority from PCT PatentApplication No. PCT/US2012/058858, filed on Oct. 5, 2012, which claimspriority from U.S. Provisional Patent Application No. 61/544,202, filedon Oct. 6, 2011, which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to systems and methods forsubsurface oil recovery optimization. More particularly, the inventionrelates to subsurface secondary and/or tertiary oil recoveryoptimization based on either short term, medium term or long termoptimization analysis of selected zones, wells patterns/clusters and/orfields.

BACKGROUND OF THE INVENTION

Various systems and methods are well known for maximizing subsurfacesecondary and/or tertiary oil recovery. Current systems for maximizingsecondary and/or tertiary recovery generally rely on many steps, indifferent systems, and software tools, which users need to setup andmanage by themselves. This is a manual process, where the user willcreate a numerical analysis model of the reservoir, run the model with afew different operating decisions and/or parameters, analyze the resultsand choose the best answer. The unautomated process often requiresrunning multiple applications, which are not integrated, to obtainresults to be integrated. As a result of the different applicationsrequired, a significant amount of reformatting data between applicationsmay be necessary, creating further labor and the potential for error.Moreover, as the process is manually performed in numerous locations,there is no electronic audit trail for later review. This may be furthercomplicated as analysis tools are generally generic and not designed tointegrate data and to provide and assess simulations according tovarying criteria. Current systems provide very little feedback as to thequality of the model and checking to make sure that the results arerealistic. They do not provide interactive graphical feedback to theuser at various levels of field operations and they do not provide trueoptimization and decision support tools. They also do not leverage thetrue value of real time data from the field. As a result, currentsystems are manual, labor intensive, and require transfer of data fromone system to another while requiring the users to verify that theoutput from one system is usable as the input to another system. Thesedeficiencies in current systems mean that the number of people who cando this type of work is quite limited. As a result, this process isperformed by a limited number of experts within an organization. With acurrently available set of tools, even these experts take a very longtime to perform the process and are prone to errors because of themanual nature of the process.

As a result of the limitations of current systems, users generally donot look at multiple scenarios to take into account possibleuncertainties in the underlying numerical reservoir model. Nor to usersexhaustively utilize optimization technologies to analyze, rank andchoose the best development operations to increase secondary and/ortertiary oil recovery. This often precludes users from addressinguncertainties in a reservoir model by periodically reassessing selectedscenarios based on data such as historical performance of the reservoir,patterns, wells, and/or zones or other data. Moreover, in addition toall the limitations listed above, current systems do not provide goodtools to allow a user to update a model, or series of models. Thesedifficulties in generating a first model serve as a deterrent togeneration of later updates.

Nor do current systems address the overall performance of the field oreffectiveness of secondary or tertiary recovery processes. Practitionersof the current processes will generally recognize that sweep efficiencyis an important metric of recovery process effectiveness. Sweepefficiency can be calculated at different locations in a field and atdifferent scales. For example, sweep efficiency could be calculatedlocally near a well, at a zone level, between two wells, at a patternlevel, at a field level and at different levels in between. Currently,there is no good method to measure or calculate sweep efficiency healthindicators. There is also no integrated system and method forsimultaneous simulation and optimization of well production at differentscales or ranks from the field to equipment levels.

SUMMARY OF THE INVENTION

The present invention therefore, meets the above needs and overcomes oneor more deficiencies in the prior art by providing systems and methodsfor subsurface secondary and/or tertiary oil recovery optimization basedon either short term, medium term or long term optimization analysis ofselected zones, wells patterns/clusters and/or fields.

In one embodiment, the present invention includes a method for long termoil recovery optimization, which comprises: i) selecting one or morezones, wells, patterns/clusters or fields; ii) displaying multipleoptimization scenarios and corresponding actions for optimization of theone or more selected zones, wells, patterns/clusters or fields during anevaluation of a plan for developing a field; iii) selecting one or moreof the optimization scenarios and displaying each corresponding action;iv) selecting a prediction date for each selected optimization scenario;and v) displaying the one or more selected optimization scenarios, theeffect of each corresponding action on the one or more selected zones,wells, patterns/clusters or fields on the prediction date, and anupdated field development plan using a computer system, the updatedfield development plan being displayed for a field with a respective netpresent value calculation and projected production parameters.

In another embodiment, the present invention includes a program carrierdevice for carrying computer executable instructions for long term oilrecovery optimization. The instructions are executable to implement: i)selecting one or more zones, wells, patterns/clusters or fields; ii)displaying multiple optimization scenarios and corresponding actions foroptimization of the one or more selected zones, wells, patterns/clustersor fields during an evaluation of a plan for developing a field; iii)selecting one or more of the optimization scenarios and displaying eachcorresponding action; iv) selecting a prediction date for each selectedoptimization scenario; and v) displaying the one or more selectedoptimization scenarios, the effect of each corresponding action on theone or more selected zones, wells, patterns/clusters or fields on theprediction date, and an updated field development plan, the updatedfield development plan being displayed for a field with a respective netpresent value calculation and projected production parameters.

Additional aspects, advantages and embodiments of the invention willbecome apparent to those skilled in the art from the followingdescription of the various embodiments and related drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described below with references to theaccompanying drawings in which like elements are referenced with likereference numerals, and in which:

FIG. 1 illustrates an overall process for subsurface oil recoveryoptimization according to the present invention.

FIG. 2 is a flow diagram illustrating one embodiment of a method forimplementing the present invention.

FIG. 3 is a flow diagram illustrating one embodiment of a method forperforming step 216 in FIG. 2.

FIG. 4 is a flow diagram illustrating one embodiment of a method forperforming step 220 in FIG. 2.

FIG. 5 is a flow diagram illustrating one embodiment of a method forperforming step 224 in FIG. 2.

FIG. 6 is a block diagram illustrating one embodiment of a system forimplementing the present invention.

FIG. 7 is an exemplary graphical user interface illustrating step 204 inFIG. 2.

FIG. 8 is an exemplary graphical user interface illustrating step 206 inFIG. 2.

FIG. 9 is an exemplary graphical user interface illustrating step 306 inFIG. 3.

FIG. 10 is an exemplary graphical user interface illustrating step 324in FIG. 3.

FIG. 11 is an exemplary graphical user interface illustrating step 406in FIG. 4.

FIG. 12 is an exemplary graphical user interface illustrating step 412in FIG. 4.

FIG. 13 is an alternative exemplary graphical user interfaceillustrating step 412 in FIG. 4.

FIG. 14 is an exemplary graphical user interface illustrating step 422in FIG. 4.

FIG. 15 is a table illustrating exemplary levels of optimizationprovided by the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The subject matter of the present invention is described withspecificity, however, the description itself is not intended to limitthe scope of the invention. The subject matter thus, might also beembodied in other ways, to include different steps or combinations ofsteps similar to the ones described herein, in conjunction with otherpresent or future technologies. Moreover, although the term “step” maybe used herein to describe different elements of methods employed, theterm should not be interpreted as implying any particular order among orbetween various steps herein disclosed unless otherwise expresslylimited by the description to a particular order. While the followingdescription refers to the oil and gas industry, the systems and methodsof the present invention are not limited thereto and may also be appliedin other industries to achieve similar results.

The present invention includes systems and methods for optimizing oilrecovery, by reducing unwanted fluid/gas production, reducing workoverdowntime, reducing by-passed oil and gas, and maximizing net presentvalue through optimization of injection and production profiles. Thesystems and methods therefore, consider intelligent manipulation ofsubsurface displacement profiles; surface and facility optimizationconstraints, well intervention/recompletion designs, and dynamic fielddevelopment planning through decisions to drill and design newproducer/injector/observation wells.

The systems and methods perform all permutations and combinations withsurveillance, diagnostics and optimization from a micro to a macro scalespanning from the equipment level to the zone level, to well level to apattern/cluster level to, finally, the reservoir/field level. Thesystems and methods allow the user to perform present and/or predictivediagnostics on the field and/or sweep efficiency health, as well asadvise the user of optimum optimization actions for short, medium andlong term time frames. The systems and methods allow the user tointeractively perform comparative “what if” scenarios (war games) withthe previously advised optimization actions, generate appropriatebusiness cases and thus, take and implement the appropriate optimizationactions that help maximize oil recovery and economic value.

The systems and methods utilize real-time surveillance field data toprovide advanced value of integrated asset management, which provides anautomated advisory for short, medium and/or long termmultiple-well/pattern and field level optimization. The systems andmethods allow personnel to perform predictive analysis on the effect ofselected optimization actions, and deliver an intuitive user interfacefor enhanced collaborative decision making between asset, reservoir,operations, and production personnel. The systems and methods,therefore, obviate the need for labor intensive simulation andoptimization in separate actions.

In short, the systems and methods enable monitoring of the subsurfacehealth of a production field and provide automated advisory on proactivereservoir diagnostics with tangible optimization actions, thuspermitting forecasted analysis on the proposed reservoir optimizationactions.

Method Description

Referring now to FIG. 1, an overall process 100 for subsurface oilrecovery according to the present invention is illustrated.

In step 102, the process 100 identifies present field health. Oneembodiment of a method for identifying field health today is illustratedby step 202 in FIG. 2.

In step 104, the process 100 predicts field health. One embodiment of amethod for field health prediction is illustrated by steps 204-208 inFIG. 2.

In step 106, the process 100 diagnoses field health for today and thefuture, which may include identifying and detecting the bypassed andunswept oil spots using a mobile water saturation function. Oneembodiment of a method for diagnosing field health for today and thefuture is illustrated by step 210 in FIG. 2.

In step 108, the process 100 advises optimization for short, medium, andlong terms, if optimization is desired. One embodiment of a method fordetermining the desired optimization is illustrated by steps 212, 214,218, and 222 in FIG. 2.

If optimization is desired, then the user must also select whether thetime-frame for optimization will be short term, medium term, or longterm. If short term optimization is desired, then one embodiment of amethod for short term optimization is illustrated by steps 302-306 inFIG. 3. If medium term optimization is desired, then one embodiment of amethod for medium term optimization is illustrated by steps 402-406 inFIG. 4. If long term optimization is desired, then one embodiment of amethod for long term optimization is illustrated by steps 502-506 inFIG. 5.

Optimization may be provided as an automated advisory for reactive andproactive optimization of sweep efficiency to achieve key performancetargets—including time horizons (from 1 day to any number of years),reducing water handling (as a percentage), reducing downtime forworkover times (as a percentage), reducing by-passed oil, and increasingrecovery from new wells and recompletions (as a percentage).Optimization may also enable timely decisions based on real-time data toprovide updated, predictive models and provide expert system andoptimized advisories.

In step 110, the process 100 includes “what if” scenarios to assess andcompare various optimization scenarios, which may also be regarded asoptimization war games. One embodiment of a method for conductingoptimization “what if” scenarios is illustrated by steps 308-316 in FIG.3 for short term optimization, steps 408-416 in FIG. 4 for medium termoptimization, and steps 508-516 in FIG. 5 for long term optimization.

In step 112, the process 110 implements the optimization. One embodimentof a method for obtaining or seeking optimization implementation isillustrated by steps 318-326 in FIG. 3 for short term optimization,steps 418-426 in FIG. 4 for medium term optimization, and steps 518-526in FIG. 5 for long term optimization.

The overall process 100 therefore, provides a fully integratedsubsurface reservoir management solution for improving sweep efficiencyand allowing reservoir and production personnel (likely engineers) tocollaborate. This may be accomplished while monitoring reservoirdynamics during production, utilizing surface and downhole sensors,updating and simulating the reservoir and well models. This may providecontrol strategies for short-production optimization and increasedrecovery utilizing surface chokes, ICD's and smartwells whileimplementing optimization strategies on future planning, such as infilldrilling to recover bypassed oil.

The process 100 for optimization may be reactive, simple proactive, orenhanced proactive (“proactive plus”). Reactive optimization may becharacterized as an immediate reaction to current conditions. Reactiveoptimization may occur in the short term and may be directed to actionssuch as optimizing choke settings and production/injection rates. Simpleproactive optimization may be characterized as an action based onpredicted conditions, such as to predict fluid movement away from thewellbore and therefore, to optimize subsurface operations by takingmeasures such as choking a downhole valve setting in order to increasetotal recovery. Simple proactive optimization also focuses on long termfield development planning optimization such as scheduling future infilldrilling producer/injector locations, workovers, their configurations,etc. Enhanced proactive optimization, on the other hand, provides forright time integration of exploration, drilling, completion andproduction disciplines while evaluating the appropriate plan of actionfor developing a field to ensure there is sufficient time afteroptimization options are identified that might effect them. Simpleproactive optimization may occur over the medium term to long term (suchas, but not limited to, three months to 2 years) and include the actionsof reactive optimization together with short term to medium term fielddevelopment plan updates. Thus, integration involves running severalreservoir depletion scenarios as well as cost/benefit analysisscenarios, in real time, thus helping plan the best integrated solutionacross all disciplines of an asset development life cycle. Enhancedproactive optimization for example, could allow the operator to changecompletion and production planning in real time for better ultimatedepletion, while actually drilling and gathering additional informationabout the reservoir. The goals of each of these exemplary levels ofoptimization are illustrated by the table 1500 in FIG. 15.

Thus, the process 100 depends on right time reservoir managementincluding continuous reservoir visualization, proactive reservoirdiagnostics and optimization, and predictive reservoir optimizationanalysis.

Referring now to FIG. 2, a flow diagram illustrates one embodiment of amethod 200 for implementing the present invention.

In step 201, current conditions data or previously computed scenarioconditions data are selected using the client interface and/or the videointerface described in reference to FIG. 6. Selection of whether to usecurrent conditions data or previously computed scenario conditions datamay be based on a subjective determination of whether to use currentconditions or previous optimizations. Current conditions data providesthe ability to assess the present health of the field and to performoptimizations based on that data. Previously computed scenarioconditions data provides the ability to review the past health of thefield in relation to current health and to perform optimizations basedon saved data, which may include optimized short, medium or even longterm data.

In step 202, the current sweep efficiency health is displayed usingtechniques well known in the art and the video interface described inreference to FIG. 6. Subsurface visualization techniques and currentsweep efficiency health indicators, for example, may be used withintegrated current conditions data, previously computed scenarioconditions data and historic data to provide a display of ranked zones,wells, patterns/sectors and/or fields representing the current sweepefficiency health. Effective subsurface visualization requiresvisualization of the reservoir dynamics as the subsurface changes in thewellbore, near the wellbore and away from the wellbore. A goal ofsubsurface visualization is to create a very high resolution threedimensional (3-D) visualization interface, which may include variousfeatures including fiber optic monitoring visualization, surfacedeformation visualization, 3D fluid displacement visualization, bypassedoil 3D visualization, oil/water interface visualization, streamlinesvisualization, field/zone/well maps, isobaric maps, saturation maps,injection patterns at subsurface zone-levels, and zone level allocationsof production/injection.

In step 204, a future date for the prediction of sweep efficiency healthwithout optimization and the number of intervening periods are selectedusing the client interface and/or the video interface described inreference to FIG. 6. Selection of the future date and interveningperiods is subjective and is based on the preference and/or experienceof the user. One example of a future date selected for the prediction ofsweep efficiency health without optimization and the number ofintervening periods is illustrated by the graphical user interface 700in FIG. 7, which illustrates a future date four (4) years in the futureand intervening periods of one year.

In step 206, displays of the predicted sweep efficiency health at theselected future date and at the end of each of the intervening periodsare generated using techniques well known in the art and the videointerface described in reference to FIG. 6. The displays include a rankof the sweep efficiency health for the identified zones, wells,patterns/sectors and/or fields as well as other potential user-definedspatial scales. One example of a display of the predicted sweepefficiency health at a selected future date and at the end of eachintervening period selected for FIG. 7 is illustrated by the graphicaluser interface 800 in FIG. 8.

In step 208, one of the displays of the predicted sweep efficiencyhealth or the display of the current sweep efficiency health is selectedusing the client interface and/or the video interface described inreference to FIG. 6. Each selected display may provide further detail,including the history for sweep efficiency health indicators at anyscale of the zone, well, pattern/sector and/or field.

In step 210, the cause of any undesirable sweep efficiency healthindicators for the selected sweep efficiency health display is diagnosedusing well known diagnostic techniques, such as those found in theDECISIONSPACE™ software for reservoir simulation. The cause may bedisplayed by an automated advisory feature that utilizes indicatorsincluding volumetric efficiency, voidage replacement, displacementefficiency, nominal pressure and wellbore capture factor (Fcap) layer bylayer in the reservoir. The cause can also be diagnosed by comparingcurrent conditions data with historic data or previously computedscenario conditions data. Various diagnostics can also be performed byevaluating a flow or production index that is normalized by a length ofthe perforating interval. A streamline numerical calculation can also beused to estimate correlation factors and well allocation factors.

In step 212, the method 200 determines whether optimization analysis ofproduction is desired based on the results of step 210. If optimizationanalysis is desired, then the method 200 proceeds to step 214.Alternatively, the method 200 may proceed to steps 218 or 222 ifoptimization analysis is desired. Optimization analysis may be desired,for example, if the cause of any undesirable sweep efficiency healthindicators is identified by the diagnostic performed in step 210.Otherwise, optimization analysis may not be desired if there are noundesirable sweep efficiency health indicators. If optimization analysisis not desired, then the method 200 ends.

In step 214, the method 200 determines if short term optimizationanalysis is desired based on the results of step 210 and whether thecause of any undesirable sweep efficiency health indicators can beimmediately resolved (e.g. by adjusting a choke). If short termoptimization analysis is not desired, then the method 200 proceeds tostep 218. Alternatively, the method 200 may proceed to step 222 if shortterm optimization analysis is not desired. If short term optimizationanalysis is desired, then the method 200 proceeds to step 216.

In step 216, short term optimization is performed. One embodiment of amethod for performing short term optimization is illustrated in FIG. 3.

In step 218, the method 200 determines if medium term optimizationanalysis is desired based on the results of step 210 and whether thecause of any undesirable sweep efficiency health indicators cannot beresolved immediately but may be resolved within a matter of a day up toa few months (e.g. equipment repair). If medium term optimizationanalysis is not desired, then the method 200 proceeds to step 222.Alternatively, the method 200 may proceed to step 214 if medium termoptimization analysis is not desired. If medium term optimizationanalysis is desired, then the method 200 proceeds to step 220.

In step 220, medium term optimization is performed. One embodiment of amethod for performing medium term optimization is illustrated in FIG. 4.

In step 222, the method 200 determines if long term optimizationanalysis is desired based on the results of step 210 and whether thecause of any undesirable sweep efficiency health indicators cannot beresolved immediately or in a few months but may be resolved within ayear or longer (e.g. drilling new wells). The decision betweenperforming short term optimization analysis, medium term optimizationanalysis or long term optimization analysis is subjectively based on theexperiences and expertise of the person making the decision. If longterm optimization analysis is not desired, then the method 200 ends.Alternatively, the method 200 may proceed to step 214 or step 218 iflong term optimization analysis is not desired. If long termoptimization analysis is desired, then the method 200 proceeds to step224.

In step 224, long term optimization is performed. One embodiment of amethod for performing long term optimization is illustrated in FIG. 5.

Referring now to FIG. 3, a flow diagram illustrates one embodiment of amethod 300 for performing step 216 in FIG. 2.

In step 302, all zones, wells, patterns/clusters and/or fields to beoptimized are selected from the selected sweep efficiency health displayusing the client interface and/or the video interface described inreference to FIG. 6.

In step 304, a series of ranked optimization scenarios and correspondingactions for reactive optimization are displayed using the videointerface described in reference to FIG. 6 and techniques well known inthe art. The series of ranked optimization scenarios and correspondingactions for reactive optimization are based on the optimization of theselected zones, wells, patterns, clusters and/or fields, which may beexported to a net present value calculator. Thousands of optimizationscenarios can be created by reservoir simulation or utilizing proxymodels.

In step 306, one or more optimization scenarios may be selected and thecorresponding action for the optimization of the selected zones, wells,patterns/clusters and/or fields is displayed using the client interfaceand/or the video interface described in reference to FIG. 6. One exampleof a display of the corresponding action is illustrated by the graphicaluser interface 900 in FIG. 9.

In step 310, a prediction date for each selected optimization scenariomay be selected using the client interface and/or the video interfacedescribed in reference to FIG. 6. The prediction date determines theperiod of time each respective selected optimization scenario is run ona simulator.

In step 312, the one or more selected optimization scenarios and theeffect of each corresponding action on the selected zones, wells,patterns/clusters and/or fields on the prediction date is displayedusing the video interface described in reference to FIG. 6. The displaymay include, for example, changes in sweep efficiency health indicators,various subsurface visualization parameters for the selected zones,wells, patterns/clusters and/or fields, and various net present valuederivatives for each selected optimization scenario.

In step 314, the method 300 determines whether optimization is desiredbased on the results of step 312. If optimization is desired, then themethod 300 proceeds to step 316. If optimization is not desired, thenthe method 300 proceeds to step 318.

In step 316, the desired optimization scenario(s) may be selected fromthe one or more selected optimization scenarios for implementation usingthe client interface and/or the video interface described in referenceto FIG. 6.

In step 318, the data underlying the results of step 312 is saved.

In step 320, the data underlying the results of step 312 selected instep 316 for implementation is saved.

In step 322, the method 300 determines whether the user has actionapproval to unilaterally implement the desired optimization scenario(s).If the user does not have the action approval, then the method 300proceeds to step 324. If the user has action approval, then the method300 proceeds to step 326.

In step 324, a request for implementation of the desired optimizationscenario(s) may be generated and/or sent with a business case report,recommendation and analysis using the client interface and/or the videointerface described in reference to FIG. 6. One example of a request forimplementation of the desired application scenario(s) is illustrated bythe graphical user interface 1000 in FIG. 10.

In step 326, the corresponding action(s) for each desired optimizationscenario to be implemented may be remotely executed or approved formanual implementation using the client interface and/or the videointerface described in reference to FIG. 6.

Referring now to FIG. 4, a flow diagram illustrates one embodiment of amethod 400 for performing step 220 in FIG. 2.

In step 402, all zones, wells, patterns/clusters and/or fields to beoptimized are selected from the selected sweep efficiency health displayusing the client interface and/or the video interface described inreference to FIG. 6.

In step 404, a series of ranked optimization scenarios and correspondingactions for proactive optimization are displayed using the videointerface described in reference to FIG. 6 and techniques well known inthe art. The series of ranked optimization scenarios and correspondingactions for proactive optimization are based on the optimization of theselected zones, wells, patterns, clusters and/or fields, which may beexported to a net present value calculator. The optimization actionscould be actions such as workovers/recompletions, conformance, surfaceinstrumentation and others.

In step 406, one or more optimization scenarios may be selected and thecorresponding action for the optimization of the selected zones, wells,patterns/clusters and/or fields is displayed using the client interfaceand/or the video interface described in reference to FIG. 6. One exampleof selecting one or more optimization scenarios is illustrated by thegraphical user interface 1100 in FIG. 11.

In step 410, a prediction date for each selected optimization scenariomay be selected using the client interface and/or the video interfacedescribed in reference to FIG. 6. The prediction date determines theperiod of time each respective selected optimization scenario is run ona simulator.

In step 412, the one or more selected optimization scenarios, the effectof each corresponding action on the selected zones, wells,patterns/clusters and/or fields on the prediction date, and an updatedfield development plan for the field with the respective net presentvalue calculation and projected production parameters are displayedusing the video interface described in reference to FIG. 6. The displaymay include, for example, changes in sweep efficiency health indicators,various subsurface visualization parameters for the selected zones,wells, patterns/clusters and/or fields, and various net present valuederivatives for each selected optimization scenario. One example of sucha display is illustrated by the graphical user interface 1200 and 1300in FIGS. 12 and 13, respectively.

In step 414, the method 400 determines whether optimization is desiredbased on the results of step 412. If optimization is desired, then themethod 400 proceeds to step 416. If optimization is not desired, thenthe method 400 proceeds to step 418.

In step 416, the desired optimization scenario(s) may be selected fromthe one or more selected optimization scenarios for implementation usingthe client interface and/or the video interface described in referenceto FIG. 6.

In step 418, the data underlying the results of step 412 is saved.

In step 420, the data underlying the results of step 412 selected instep 416 for implementation is saved.

In step 422, the method 400 determines whether the user has actionapproval to unilaterally implement the desired optimization scenario(s).If the user does not have the action approval, then the method 400proceeds to step 424. If the user has action approval, then the method400 proceeds to step 426. One example of action approval to implementthe desired optimization scenarios) is illustrated by the graphical userinterface 1400 in FIG. 14.

In step 424, a request for implementation of the desired optimizationscenario(s) may be generated and/or sent with a business case report,recommendation and analysis using the client interface and/or the videointerface described in reference to FIG. 6.

In step 426, the corresponding action(s) for each desired optimizationscenario to be implemented may be remotely executed or approved formanual implementation using the client interface and/or the videointerface described in reference to FIG. 6.

Referring now to FIG. 5, a flow diagram illustrates one embodiment of amethod 500 for performing step 224 in FIG. 2.

In step 502, all zones, wells, patterns/clusters and/or fields to beoptimized are selected from the selected sweep efficiency health displayusing the client interface and/or the video interface described inreference to FIG. 6.

In step 504, a series of ranked optimization scenarios and correspondingactions derived from right time (the desired future point in time—short,medium or long term) integration of exploration, drilling, completionand production disciplines for enhanced proactive (proactive plus)optimization are displayed using the video interface described inreference to FIG. 6 and techniques well known in the art whileevaluating the appropriate plan of action for developing a field. Theseries of ranked optimization scenarios and corresponding actions forproactive plus optimization are based on the optimization of theselected zones, wells, patterns, clusters and/or fields, which may beexported to a net present value calculator.

In step 506, one or more optimization scenarios may be selected and thecorresponding action for the optimization of the selected zones, wells,patterns/clusters and/or fields is displayed using the client interfaceand/or the video interface described in reference to FIG. 6.

In step 510, a prediction date for each selected optimization scenariomay be selected using the client interface and/or the video interfacedescribed in reference to FIG. 6. The prediction date determines theperiod of time each respective selected optimization scenario is run ona simulator.

In step 512, the one or more selected optimization scenarios, the effectof each corresponding action on the selected zones, wells,patterns/clusters and/or fields on the prediction date, and an updatedfield development plan for the field with the respective net presentvalue calculation and projected production parameters are displayedusing the video interface described in reference to FIG. 6. The displaymay include, for example, changes in sweep efficiency health indicators,various subsurface visualization parameters for the selected zones,wells, patterns/clusters and/or fields, and various net present valuederivatives for each selected optimization scenario. The optimizationscenarios could include actions such as long term exploration strategiesof secondary and tertiary oil recovery, infill drilling, re-drilling ofwater injection positions, and other field development actions.

In step 514, the method 500 determines whether optimization is desiredbased on the results of step 512. If optimization is desired, then themethod 500 proceeds to step 516. If optimization is not desired, thenthe method 500 proceeds to step 518.

In step 516, the desired optimization scenario(s) may be selected fromthe one or more selected optimization scenarios for implementation usingthe client interface and/or the video interface described in referenceto FIG. 6.

In step 518, the data underlying the results of step 512 is saved.

In step 520, the data underlying the results of step 512 selected instep 516 for implementation is saved.

In step 522, the method 500 determines whether the user has actionapproval to unilaterally implement the desired optimization scenario(s).If the user does not have the action approval, then the method 500proceeds to step 524. If the user has action approval, then the method500 proceeds to step 526.

In step 524, a request for implementation of the desired optimizationscenario(s) may be generated and/or sent with a business case report,recommendation and analysis using the client interface and/or the videointerface described in reference to FIG. 6.

In step 526, the corresponding action(s) for each desired optimizationscenario to be implemented may be remotely executed or approved formanual implementation using the client interface and/or the videointerface described in reference to FIG. 6.

System Description

The present invention may be implemented through a computer-executableprogram of instructions, such as program modules, generally referred tosoftware applications or application programs executed by a computer.The software may include, for example, routines, programs, objects,components, and data structures that perform particular tasks orimplement particular abstract data types. The software forms aninterface to allow a computer to react according to a source of input.DECISIONSPACE, which is a commercial software application marketed byLandmark Graphics Corporation, may be used as an interface applicationto implement the present invention. The software may also cooperate withother code segments to initiate a variety of tasks in response to datareceived in conjunction with the source of the received data. Other codesegments may provide optimization components including, but not limitedto, neural networks, earth modeling, history matching, optimization,visualization, data management, reservoir simulation and economics. Thesoftware may be stored and/or carried on any variety of memory such asCD-ROM, magnetic disk, bubble memory and semiconductor memory (e.g.,various types of RAM or ROM). Furthermore, the software and its resultsmay be transmitted over a variety of carrier media such as opticalfiber, metallic wire, and/or through any of a variety of networks, suchas the Internet.

Moreover, those skilled in the art will appreciate that the inventionmay be practiced with a variety of computer-system configurations,including hand-held devices, multiprocessor systems,microprocessor-based or programmable-consumer electronics,minicomputers, mainframe computers, and the like. Any number ofcomputer-systems and computer networks are acceptable for use with thepresent invention. The invention may be practiced indistributed-computing environments where tasks are performed byremote-processing devices that are linked through a communicationsnetwork. In a distributed-computing environment, program modules may belocated in both local and remote computer-storage media including memorystorage devices. The present invention may therefore, be implemented inconnection with various hardware, software or a combination thereof, ina computer system or other processing system.

Referring now to FIG. 6, a block diagram illustrates one embodiment of asystem for implementing the present invention on a computer. The systemincludes a computing unit, sometimes referred to as a computing system,which contains memory, application programs, a client interface, a videointerface, and a processing unit. The computing unit is only one exampleof a suitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.

The memory primarily stores the application programs, which may also bedescribed as program modules containing computer-executableinstructions, executed by the computing unit for implementing thepresent invention described herein and illustrated in FIG. 2. The memorytherefore, includes a subsurface oil recovery optimization module, whichenables the methods illustrated and described in reference to FIG. 2 andintegrates functionality from the remaining application programsillustrated in FIG. 6. The subsurface oil recovery optimization module,for example, may be used, unlike the prior art, to execute many of thefunctions described in reference to steps 201, 202, 204, 206 (as todisplay), 208, 210, 212, 214, 218, 222, 302, 304 (as to display andranking), 306 (user selection), 308 (as to selection), 310, 312 (as todisplay), 314, 316, 318, 320, 322, 324, 326, 402, 404 (as to display andranking), 406 (user selection), 408 (as to selection), 410, 412 (as todisplay), 414, 416, 418, 420, 422, 424, 426, 502, 504 (as to display andranking), 506 (user selection), 508 (as to selection), 510, 512 (as todisplay), and 514, 516, 518, 520, 522, 524, 526 in FIGS. 2, 3, 4 and 5.The memory also includes DECISIONSPACE, which may be used, for example,as an interface application to execute the functions described inreference to steps 206 (as to predicted sweep efficiency health), 304(as the computation of ranked scenarios), 306 (advised actions), 308 (asto effects), 312 (as to predicted changes in sweep efficiency healthindicators), 404 (as the computation of ranked scenarios), 406 (advisedactions), 408 (as to effects), 412 (as to predicted changes in sweepefficiency health indicators), 504 (as the computation of rankedscenarios), 506 (advised actions), 508 (as to effects), and 512 (as topredicted changes in sweep efficiency health indicators) in FIGS. 2, 3,4 and 5. Although DECISIONSPACE may be used as an interface application,other interface applications may be used, instead, or the subsurface oilrecovery optimization module may be used as a stand-alone application.

Although the computing unit is shown as having a generalized memory, thecomputing unit typically includes a variety of computer readable media.By way of example, and not limitation, computer readable media maycomprise computer storage media and communication media. The computingsystem memory may include computer storage media in the form of volatileand/or nonvolatile memory such as a read only memory (ROM) and randomaccess memory (RAM). A basic input/output system (BIOS), containing thebasic routines that help to transfer information between elements withinthe computing unit, such as during start-up, is typically stored in ROM.The RAM typically contains data and/or program modules that areimmediately accessible to and/or presently being operated on by theprocessing unit. By way of example, and not limitation, the computingunit includes an operating system, application programs, other programmodules, and program data.

The components shown in the memory may also be included in otherremovable/non-removable, volatile/nonvolatile computer storage media orthey may be implemented in the computing unit through an applicationprogram interface (“API”) or cloud computing, which may reside on aseparate computing unit connected through a computer system or network.For example only, a hard disk drive may read from or write tonon-removable, nonvolatile magnetic media, a magnetic disk drive mayread from or write to a removable, nonvolatile magnetic disk, and anoptical disk drive may read from or write to a removable, nonvolatileoptical disk such as a CD ROM or other optical media. Otherremovable/non-removable, volatile/nonvolatile computer storage mediathat can be used in the exemplary operating environment may include, butare not limited to, magnetic tape cassettes, flash memory cards, digitalversatile disks, digital video tape, solid state RAM, solid state ROM,and the like. The drives and their associated computer storage mediadiscussed above provide storage of computer readable instructions, datastructures, program modules and other data for the computing unit.

A client may enter commands and information into the computing unitthrough the client interface, which may be input devices such as akeyboard and pointing device, commonly referred to as a mouse, trackballor touch pad. Input devices may include a microphone, joystick,satellite dish, scanner, voice recognition or gesture recognition, orthe like. These and other input devices are often connected to theprocessing unit through a system bus, but may be connected by otherinterface and bus structures, such as a parallel port or a universalserial bus (USB).

A monitor or other type of display device may be connected to the systembus via an interface, such as a video interface. A graphical userinterface (“GUI”) may also be used with the video interface to receiveinstructions from the client interface and transmit instructions to theprocessing unit. In addition to the monitor, computers may also includeother peripheral output devices such as speakers and printer, which maybe connected through an output peripheral interface.

Although many other internal components of the computing unit are notshown, those of ordinary skill in the art will appreciate that suchcomponents and their interconnection are well known.

While the present invention has been described in connection withpresently preferred embodiments, it will be understood by those skilledin the art that it is not intended to limit the invention to thoseembodiments. It is therefore, contemplated that various alternativeembodiments and modifications may be made to the disclosed embodimentswithout departing from the spirit and scope of the invention defined bythe appended claims and equivalents thereof.

The invention claimed is:
 1. A computer-implemented method, whichcomprises: receiving first input corresponding to a selection of one ormore zones, wells, patterns, clusters or fields; identifying a fielddevelopment plan associated with the one or more selected zones, wells,patterns, clusters or fields, the field development plan correspondingto a sweep efficiency health indicator; displaying multiple scenariosand one or more long-term optimization actions corresponding to eachscenario of the multiple scenarios, each of the one or more long-termoptimization actions optimizing hydrocarbon fluid recovery for the oneor more selected zones, wells, patterns, clusters or fields, each of theone or more long-term optimization actions being responsive to apredicted future condition, the multiple displayed scenarios and thecorresponding one or more long-term optimization actions being rankedaccording to at least one of net present value, increased oil recovery,or reduced recovery of unwanted gas or fluids, and the ranking beingbased at least in part on real-time surveillance field data associatedwith the one or more zones, wells, patterns, clusters or fields;receiving second input corresponding to a selection of one or more ofthe multiple scenarios, and displaying each corresponding long-termoptimization action of the corresponding one or more selected scenarios;receiving third input corresponding to a selection of a prediction datefor each selected scenario; executing a simulation for each selectedscenario based on the prediction date, the execution of the simulationusing one or more predictive models to predict a condition that issimulated to exist on the prediction date, the condition beingassociated with the one or more zones, wells, patterns, clusters orfields, and the condition resulting from each selected scenario existingfor a period of time; selecting a long-term optimization actionresponsive to the simulated condition occurring on the prediction date,the long-term optimization action being selected from the one or morelong-term optimization actions corresponding to one or more selectedscenarios; updating, based on the predicted condition, the fielddevelopment plan, the selected long-term optimization action respondingto the predicted condition and causing the sweep efficiency healthindicator to be modified, and the modified sweep efficiency healthindicator representing an improvement over the sweep efficiency healthindicator; and displaying, using one or more processors, each of: theone or more selected scenarios, an effect of the determined long-termoptimization action on the prediction date, and the updated fielddevelopment plan, the updated field development plan being displayed forthe field with a respective net present value calculation and themodified sweep efficiency health indicator, and the updated fielddevelopment plan including an updated schedule for infill andre-drilling of water injection positions.
 2. The method of claim 1,further comprising determining if enhancement is desired based on theone or more selected scenarios, the effect of the determined long-termoptimization action on the one or more selected zones, wells, patterns,clusters or fields and the updated field development plan.
 3. The methodof claim 2, further comprising selecting one or more desired scenariosfrom the one or more selected scenarios for implementation.
 4. Themethod of claim 3, further comprising executing the correspondinglong-term optimization action of the corresponding actions for eachdesired scenario as a long term enhancement response to an undesirablesweep efficiency health indicator.
 5. The method of claim 4, wherein thecorresponding long-term optimization action for each scenario isremotely executed.
 6. The method of claim 3, further comprising sendingan approval for manual implementation of the corresponding long-termoptimization action for each scenario.
 7. The method of claim 3, furthercomprising sending a request for the implementation of the correspondinglong-term optimization actions for each scenario with a business casereport and recommendation.
 8. The method of claim 1, wherein themultiple displayed scenarios and the corresponding long-termoptimization actions are ranked for enhanced proactive enhancement. 9.The method of claim 1, further comprising running the one or moreselected scenarios on a simulator in real-time to determine the effectof the determined long-term optimization action on the one or moreselected zones, wells, patterns, clusters or fields on the predictiondate.
 10. The method of claim 1, further comprising diagnosing a causeof an undesirable sweep efficiency health indicator for a sweepefficiency health display using streamline numerical calculation toestimate correlation factors and well allocation factors.
 11. The methodof claim 1, wherein the display of the one or more selected scenariosand the effect of the determined long-term optimization action includesinfill drilling and re-drilling of water injection positions.
 12. Anon-transitory program carrier device for carrying computer executableinstructions for long term oil recovery enhancement, the instructionsbeing executable to implement: receiving first input corresponding to aselection of one or more zones, wells, patterns, clusters or fields;identifying a field development plan associated with the one or moreselected zones, wells, patterns, clusters or fields, the fielddevelopment plan corresponding to a sweep efficiency health indicator;displaying multiple scenarios and one or more long-term optimizationactions corresponding to each scenario of the multiple scenarios, eachof the one or more long-term optimization actions optimizing hydrocarbonfluid recovery for the one or more selected zones, wells, patterns,clusters or fields, each of the one or more long-term optimizationactions being responsive to a predicted future condition, the multipledisplayed scenarios and the corresponding one or more long-termoptimization actions being ranked according to at least one of netpresent value, increased oil recovery, or reduced recovery of unwantedgas or fluids, and the ranking being based at least in part on real-timesurveillance field data associated with the one or more zones, wells,patterns, clusters or fields; receiving second input corresponding to aselection of one or more of the multiple scenarios and displaying eachcorresponding long-term optimization action of the corresponding one ormore selected scenarios; receiving third input corresponding to aselection of a prediction date for each selected scenario; executing asimulation for each selected scenario based on the prediction date, theexecution of the simulation using one or more predictive models topredict a condition that is simulated to exist on the prediction date,the condition being associated with the one or more zones, wells,patterns, clusters or fields, and the condition resulting from eachselected scenario existing for a period of time; selecting a long-termoptimization action responsive to the simulated condition occurring onthe prediction date, the long-term optimization action being selectedfrom the one or more long-term optimization actions corresponding to oneor more selected scenarios; updating, based on the predicted condition,the field development plan, the selected long-term optimization actionresponding to the predicted condition and causing the sweep efficiencyhealth indicator to be modified, and the modified sweep efficiencyhealth indicator representing an improvement over the sweep efficiencyhealth indicator; and displaying, using one or more processors, each of:the one or more selected scenarios, an effect of the determinedlong-term optimization action on the prediction date, and the updatedfield development plan, the updated field development plan beingdisplayed for the field with a respective net present value calculationand the modified sweep efficiency health indicator, and the updatedfield development plan including an updated schedule for infill andre-drilling of water injection positions.
 13. The non-transitory programcarrier device of claim 12, further comprising determining ifenhancement is desired based on the one or more selected scenarios, theeffect of the determined long-term optimization action on the one ormore selected zones, wells, patterns, clusters or fields and the updatedfield development plan.
 14. The non-transitory program carrier device ofclaim 13, further comprising selecting one or more desired scenariosfrom the one or more selected scenarios for implementation.
 15. Thenon-transitory program carrier device of claim 14, further comprisingexecuting the corresponding long-term optimization action of thecorresponding scenario for each desired scenario as a long termenhancement response to an undesirable sweep efficiency healthindicator.
 16. The non-transitory program carrier device of claim 15,wherein the corresponding long-term optimization action for eachscenario is remotely executed.
 17. The non-transitory program carrierdevice of claim 14, further comprising sending an approval for manualimplementation of the corresponding long-term optimization action foreach scenario.
 18. The non-transitory program carrier device of claim14, further comprising sending a request for the implementation of thecorresponding long-term optimization action of the correspondingscenario for each scenario with a business case report andrecommendation.
 19. The non-transitory program carrier device of claim12, wherein the multiple displayed scenarios and the correspondinglong-term optimization actions are ranked for enhanced proactiveenhancement.
 20. The non-transitory program carrier device of claim 12,further comprising running the one or more selected scenarios on asimulator in real-time to determine the effect of the determinedlong-term optimization action on the one or more selected zones, wells,patterns, clusters or fields on the prediction date.
 21. Thenon-transitory program carrier device of claim 12, further comprisingdiagnosing a cause of an undesirable sweep efficiency health indicatorfor a sweep efficiency health display using streamline numericalcalculation to estimate correlation factors and well allocation factors.22. The non-transitory program carrier device of claim 12, wherein thedisplay of the one or more selected scenarios and the effect of thedetermined long-term optimization action includes infill drilling andre-drilling of water injection positions.